Three years ago, our data governance team at a mid-sized enterprise analytics practice faced a crisis that would reshape how we approached insight generation. We were drowning in data from multiple sources—customer transaction logs, IoT sensors, social media feeds, and third-party APIs—yet our executive leadership complained they couldn't get timely answers to basic strategic questions. Our traditional ETL pipelines took days to process, our business intelligence dashboards showed stale metrics, and our small team of data scientists spent 80% of their time on data wrangling instead of actual analysis. That's when we made the decision to explore AI Agents for Data Analysis, a journey that taught us lessons no white paper or vendor pitch could have prepared us for. The promise of AI Agents for Data Analysis seemed almost too good to be true: autonomous systems that could ingest raw data from disparate sources, identify patterns humans might miss, generate predictive models on th...